A sliding-window based algorithm to determine the presence of chest compressions from acceleration data
Wolfgang J. Kern,
Simon Orlob,
Birgitt Alpers,
Michael Schörghuber,
Andreas Bohn,
Martin Holler,
Jan-Thorsten Gräsner,
Jan Wnent
Affiliations
Wolfgang J. Kern
Corresponding author.; University of Graz, Institute of Mathematics and Scientific Computing, Heinrichstr. 36, Graz, Austria; BioTechMed-Graz, Graz, Austria
Simon Orlob
BioTechMed-Graz, Graz, Austria; University Hospital Schleswig-Holstein, Institute for Emergency Medicine, Kiel, Germany; Department of Anesthesiology and Intensive Care Medicine, Division of Anesthesiology for Cardiovascular and Thoracic Surgery and Intensive Care Medicine, Medical University of Graz, Graz, Austria
Birgitt Alpers
University Hospital Schleswig-Holstein, Institute for Emergency Medicine, Kiel, Germany
Michael Schörghuber
Department of Anesthesiology and Intensive Care Medicine, Division of Anesthesiology for Cardiovascular and Thoracic Surgery and Intensive Care Medicine, Medical University of Graz, Graz, Austria
Andreas Bohn
Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany; City of Münster Fire Department, Münster, Germany
Martin Holler
University of Graz, Institute of Mathematics and Scientific Computing, Heinrichstr. 36, Graz, Austria; BioTechMed-Graz, Graz, Austria
Jan-Thorsten Gräsner
University Hospital Schleswig-Holstein, Institute for Emergency Medicine, Kiel, Germany; Department of Anaesthesiology and Intensive Care Medicine, University Hospital Schleswig-Holstein, Kiel, Germany
Jan Wnent
University Hospital Schleswig-Holstein, Institute for Emergency Medicine, Kiel, Germany; Department of Anaesthesiology and Intensive Care Medicine, University Hospital Schleswig-Holstein, Kiel, Germany; School of Medicine, University of Namibia, Windhoek, Namibia
This publication presents in detail five exemplary cases and the algorithm used in the article (Orlob et al. 2022). Defibrillator records for the five exemplary cases were obtained from the German Resuscitation Registry. They consist of accelerometry, electrocardiogram and capnography time series as well as defibrillation times, energies and impedance when recorded. For these cases, experienced physicians annotated time points of cardiac arrest and return of spontaneous circulation or termination of resuscitation attempts, as well as the beginning and ending of every single chest compression period in consensus, as described in Orlob et al. (2022). Furthermore, an algorithm was developed which reliably detects chest compression periods automatically without the time-consuming process of manual annotation. This algorithm allows for an usage in automatic resuscitation quality assessment, machine learning approaches, and handling of big amounts of data (Orlob et al. 2022).
